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Record W1973766145 · doi:10.1179/003258903225010479

Effect of tool coatings on ejection characteristics of iron powder compacts

2003· article· en· W1973766145 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePowder Metallurgy · 2003
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceLubricantCoatingMetallurgySurface finishLubricationSurface roughnessRodCore (optical fiber)CompactionLaminationPowder metallurgyComposite materialSinteringLayer (electronics)

Abstract

fetched live from OpenAlex

Powder metallurgy (PM) part makers heavily rely on part density as a mean of controlling part performance. Higher compaction pressures may be used to obtain higher densities and better properties. However,ejection stresses usually increases with compacting pressure. Those stresses may affect significantly part quality (surface finish, formation of cracks and lamination) and tool wear. Different methods may be used to minimise ejection stresses, such as the use of admixed lubricant, die wall lubrication and the modification of tool surfaces. This paper presents an approach to evaluate the effect of tool coatings on the ejection of ferrous compacts. The method consists of evaluating the ejection characteristics of core rods with different coatings. The results obtained show that ejection characteristics are sensitive to tool coatings. Coating the surface of the core rods yields important variations of the stripping pressure (2×) and ejection energy (1·6×). No clear correlations between the ejection characteristics and the part surface finish were observed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.233
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it